Secure authentication protocol for home area network in smart grid-based smart cities

Badar, Hafiz Muhammad Sanaullah; Mahmood, Khalid; Akram, Waseem; Ghaffar, Zahid; Umar, Muhammad; Das, Ashok Kumar

doi:10.1016/j.compeleceng.2023.108721

Cited by 9 publications

(2 citation statements)

References 21 publications

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“…In conclusion, the proposed leaf diseases detection system utilizes deep learning and advanced image-based prediction techniques to classify leaf images into normal or diseased categories. The integration of pre-trained models and transfer learning allows the system to efficiently detect leaf diseases [29] and contribute to the optimization of agricultural practices for better leaf yields and global food security.…”

Section: Model and Materialsmentioning

confidence: 99%

Leaf diseases detection empowered with transfer learning model

Baig,

Ul Haq,

Asif

et al. 2024

Comput. Telecommun. Eng.

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<p>The detection of leaf diseases using modern technology has significant importance in agriculture and artificial intelligence. Deep learning, specifically, plays a crucial role in this field, as it enables accurate and efficient disease classification. Early detection of leaf diseases is vital to implementing timely treatments and preventing widespread damage to leaves. Leaf diseases can be caused by various factors, including bacteria, fungi, viruses, and other pathogens. Among them, bacteria and viruses are the most invasive and can lead to substantial yield losses if not identified and treated promptly. Bacterial and viral infections are common in agricultural settings, affecting leaves of all types and ages. Our research aims to propose a transfer learning-based model for predicting leaf diseases using a dataset of leaf images. The images will be classified into healthy or diseased leaves based on extracted features. The proposed model, named Leaf Disease Transfer Learning Algorithm (LDTLA), demonstrates promising results with an average accuracy of 97.37% on the dataset. Utilizing convolutional neural networks (CNN) and deep learning techniques, our LDTLA model outperforms previous quantitative and qualitative research studies in leaf disease detection. This advanced approach to leaf disease identification holds the potential to revolutionize agriculture by enabling farmers to make informed decisions, implement targeted treatments, and minimize leaf losses caused by diseases.<strong></strong></p>

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Section: Model and Materialsmentioning

confidence: 99%

Leaf diseases detection empowered with transfer learning model

Baig,

Ul Haq,

Asif

et al. 2024

Comput. Telecommun. Eng.

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“…Brand image can increase brand trust levels and trigger brand impact, which in turn increases the level of brand loyalty [24] . Similarly, a number of researchers have performed simulations [25][26][27][28][29][30][31][32][33][34][35] . In order to maximize the significant economic benefits offered by brand equity as an interactive, market-based resource, businesses need to create brand trust [36] .…”

Section: Brand Trustmentioning

confidence: 99%

Enhancing customer satisfaction in e-commerce: The role of service quality and brand trust

Iqbal,

Tanveer,

Ul Haq

et al. 2023

Forum. Econ. Fin. Stud.

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Although online shopping has become more and more popular, keeping clients is still a big problem. Satisfaction, which is mostly dependent on comprehending and establishing trust with customers, is what determines whether or not a business can retain its customers. In the ever-changing world of e-commerce, this study examines the relationship between consumer loyalty, happiness, and service quality, with a focus on brand trust. The findings indicate that several aspects of service quality, including order processing, speed, convenience, and quality of communication from couriers, have a big impact on how satisfied customers are with their online purchases. The most powerful element influencing pleasure, however, turned out to be ease. The relationship’s key mediator is brand trust. As a result, consumer loyalty is increased and uncertainty is decreased. In particular, in developing e-commerce countries such as Pakistan, the study emphasizes how important it is to enhance consumer happiness, service quality, and brand credibility. By stressing the mediating function of brand trust and highlighting the dynamics in emerging nations, this research adds to the body of current work. It emphasizes how crucial trust and quality customer service are to creating long-lasting, customer-focused e-commerce enterprises. While acknowledging its limits, the study provides useful insights and avenues for further cross-country research.

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